Can you elaborate more on the differences in running ollama or lmstudio? Do they actually slow down the speed of the inference and if so why? Or is it just a preference thing?
Ollama and LM-Studio are fine. Their main advantage is that they have a nice way to browse models -- LMStudio from huggingface and Ollama from their own curated list. Both are great ways of getting started. Pick LM-Studio if you'd like a nice GUI frontend to mlx-lm or llama-cpp; pick ollama if you'd like a nice command line interface and don't need non-default parameters.
LM-Studio doesn't support certain parameter combinations. For instance, LM-Studio supports KV quantization....but if you're using the MLX backend, you can't set the context length when KV quantization is used? Why? Running a model with certain settings requires keeping a little SAT solver going in your head. I found that overwhelming, so I just stopped using it.
The Ollama devs want to offer a central curated experience, but I perceive their approach as "playing fast and loose." They've re-implemented unique code for every model they support in their own Go runtime, so certain parameter choices aren't supported. On my hardware, their MLX backend just doesn't work at all without segfaulting the server process for example. It doesn't smack as vibe coded the way oMLX does, but it also doesn't smack as professional or battle-tested.
Ultimately, just dropping down to llama-cpp's GGUF model support and asking for default settings has provided faster inference speeds than anything I've been able to benchmark with them, but everything's within 10% of each other anyway so it's not a huge deal for me.
You seem to understand this stuff pretty well, any recommendations on resources (blogs, YouTube channels, whatever) for software engineers that want to keep up with this stuff on this kind of level?
A lot of the content about AI out there is kind of produced to the lowest common denominator. Basically a never ending scheme of get rich quick/passive income kinds of AI content.
Unsloth’s guides on getting various models running are great starting-off points for the “practicioner’s side” of things. Note that they include settings for llama-cpp, ollama, and other runtimes in addition to their own “unsloth studio” (their product seems like overkill imo)
If you’re curious about what a particular switch does, clone the llama-cpp repository to your computer and try asking your favorite pet rock prompts like “This is llama-cpp. Can you look at what the -ctk parameter does and explain to me?” Giving Claude/codex/whatever access to the actual code goes a long way, but it is just one opinion.
If you’d like to learn how transformer-based language modeling works in detail, I suggest starting with chapter 0 or 1 of https://arena-chapter0-fundamentals.streamlit.app/ depending on your skill level, then use that to work your way to reading research papers.
Graduate students who study these topics are generally as annoyed by the “get rich quick” style of advertising as you are, so the deeper you go toward academic research the quieter those voices tend to get, mercifully. That said, this is balanced by the unfortunate fact that top labs have strong posturing signals they try to send, so it can be hard to see which preprints actually have good ideas, which are trying to promote their group’s tech instead of doing science out of curiosity, and which have authors who’ve innocently deluded themselves into overfitting their own pet projects. Read widely but adversarially, test everything but hold fast to the good stuff, etc etc
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